Method of monitoring and/or controlling a semiconductor manufacturing apparatus and system therefor

ABSTRACT

A semiconductor processing apparatus for processing a semiconductor wafer includes a plurality of sensors for monitoring a processing state, a processing result input unit, a model equation generation unit to generate a model equation for predicting a processing result, a processing result prediction unit which predicts a processing result, and a process recipe control unit. Further, a system is provided which comprises the model equation generation unit is provided at a remote location, and transmits the generated prediction model equation to the semiconductor processing apparatus through a network to control the processing condition of the semiconductor processing apparatus by the process recipe control unit.

CROSS REFERENCE TO RELATED APPLICATION

This is a continuation of U.S. application Ser. No. 10/999,006, filedNov. 30, 2004, which is a continuation of U.S. application Ser. No.10/438,842, filed May 16, 2003, now U.S. Pat. No. 6,828,165, which is acontinuation of U.S. application Ser. No. 10/196,208, filed Jul. 17,2002, now U.S. Pat. No. 6,706,543, which is a divisional of U.S.application Ser. No. 09/946,732, filed Sep. 6, 2001, now U.S. Pat. No.6,616,759, the subject matter of which is incorporated by referenceherein.

BACKGROUND OF THE INVENTION

The present invention relates to a semiconductor processing apparatus,and more particularly, to a semiconductor processing apparatus whichpredicts processing results to improve the operating rate andreliability of the apparatus and a method of monitoring and/orcontrolling the semiconductor processing apparatus.

In recent years, the dimensions of semiconductor devices have beenminiaturized more and more, so that a severe manufacturing dimensionaccuracy is required to such an extent that a gate electrode of 0.1 μmor smaller should be processed in a dimensional accuracy of 10% or less.On the other hand, in a semiconductor manufacturing apparatus forprocessing a semiconductor wafer using heat and plasma and reactionproducts, resulting from chemical reactions within the apparatus, areattached and remain on inner walls of the apparatus. Such reactionproducts change a wafer processing state in the apparatus over time. Forthis reason, as a number of wafers are sequentially processed by thesemiconductor manufacturing apparatus, the shape of semiconductordevices on wafers gradually changes to cause deteriorated performance.To accommodate this problem, generally, various countermeasures havebeen taken. For example, the inner walls of the chamber are cleanedusing plasma to remove products attached thereon, or the walls of thechamber are heated so that products are less likely to adhere on theinner walls. However, in most cases, such countermeasures are notperfect, inevitably resulting in a gradual change in the shape ofprocessed semiconductor devices. For this reason, the manufacturingapparatus must undergo replacement of parts and wet cleaning before theshape of processed devices changes so as to cause a problem. Inaddition, fluctuations in a variety of states of the apparatus involvein variations in the shape of devices processed on wafers, other thandeposited films. To address these problems, there have been createdtechniques for detecting a change in a processing state within asemiconductor manufacturing apparatus and feeding back the result ofdetection to the input of the semiconductor manufacturing apparatus tomaintain the processing state constant.

Such a method of monitoring fluctuations in plasma processing isdisclosed, for example, in JP-A-10-125660. This official document showsa method of predicting the performance of an apparatus and diagnosingthe state of plasma using an equation representing the relationshipbetween plasma processing characteristics and electric signals generatedin the apparatus. Specifically, JP-A-10-125660 discloses a method ofderiving an approximate expression which represents the relationshipbetween three electric signals and the plasma processing characteristicsof the apparatus through multiple regression. Another example isdisclosed in JP-A-11-87323. A method disclosed in JP-A-11-87323 adapts ageneral detection system having a multiplicity of existing detectorsmounted thereon to a semiconductor manufacturing apparatus to monitorthe state of the apparatus from a correlation signal of signals detectedby the detectors. Specifically, the correlation signal is generated by acalculation based on the ratio of six electric signals. A furtherexample is disclosed in U.S. Pat. No. 5,658,423. This U.S. patentdiscloses a method of monitoring the state of an apparatus by capturinga number of signals from a light and a mass analyzer to generate acorrelation signal for monitoring. The correlation signal is generatedusing a principal component analysis.

SUMMARY OF THE INVENTION

However, the method disclosed in JP-A-10-125660 fails to perform asuccessful prediction using the multiple regression when there are alarge number of sensor data for monitoring the apparatus sinceexplanatory variables include a large number of signals which are notrelated to the processing performance intended for the prediction. Themethod disclosed in JP-A-11-87323 in turn is a general method whichperforms the diagnosis using a signal correlated to a multiplicity ofdetected signals from a multiplicity of known detecting means, whereinthe correlation is established by taking the ratio of several signals,just as conventional approaches. This method, therefore, wouldencounters difficulties in applying to a specific system for accuratelymonitoring the state of a semiconductor manufacturing apparatus whichcan take a variety of states depending on a large number of causes forfluctuations. Unlike the foregoing methods, U.S. Pat. No. 5,658,423discloses a method of monitoring the state of plasma by analyzing aprincipal component of a large amount of data monitored from anapparatus to capture fluctuations in the state of the apparatus.However, semiconductor manufacturing apparatus for use in actual massproduction would not work well only with a concept of adapting a generalstatistic processing method as disclosed. For example, it is unknown inmost cases how a change in the principal component will cause what kindof result in the processing.

It is an object of the present invention to provide a semiconductorprocessing apparatus and method which monitor a processing state todetect faulty processing or predict processing results based on amonitored output to improve the operating rate and reliability of thesemiconductor processing apparatus for processing a variety of types ofdevices.

According to an aspect of the present invention, a semiconductor deviceprocessing apparatus includes a sensor for monitoring a processing stateof the semiconductor processing apparatus, processing result input meansfor inputting measured values for processing results of a semiconductorwafer processed by the semiconductor processing apparatus, a modelequation generation unit relying on sensed data acquired by the sensorand the measured values to generate a model equation for predicting aprocessing result using the sensed data as an explanatory variable, aprocessing result prediction unit for predicting a processing resultbased on the model equation and the sensed data, and a process recipecontrol unit for comparing the predicted processing result with apreviously set value to control a processing condition of thesemiconductor processing apparatus such that a deviation between thepredicted processing result and the previously set value is corrected.

With the foregoing configuration, according to the present invention,monitored data is acquired by the sensor from the semiconductorprocessing apparatus to generate a model equation which is used topredict processing results before measuring processing results ofsamples or without measuring the processing results, thereby improvingthe operating rate and reliability of the semiconductor processingapparatus.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram of a system for controlling a semiconductordevice processing apparatus illustrating one embodiment of the presentinvention;

FIG. 1B is a block diagram illustrating an exemplary modification to thecontrol system of FIG. 1A;

FIG. 2 is a block diagram illustrating another embodiment of aprocessing state monitoring unit in FIG. 1;

FIG. 3 is a block diagram illustrating an embodiment of secondaryprocessing state monitoring unit;

FIG. 4 is a flow chart illustrating an exemplary method of monitoring aprocessing state of a device;

FIG. 5A is a flow chart for explaining the operation of a modelgeneration unit;

FIG. 5B is a table illustrating a method of predicting measured valuesfor processing results of n wafers;

FIG. 6 is a graph for explaining a robust regression analysis;

FIG. 7 is a flow chart illustrating a routine for creating a modelequation;

FIG. 8 is a table for explaining a correction operation of a processrecipe control unit;

FIG. 9 is a flow chart for explaining the operation of the processingcondition generation unit; and

FIG. 10 is a diagram for explaining directions of control for processingconditions.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In the following, one embodiment of the present invention will bedescribed with reference to the accompanying drawings.

FIG. 1A illustrates a first embodiment of the present invention. In FIG.1A, a processing apparatus 1 is equipped with a processing statemonitoring unit 2. The processing state monitoring unit 2 may beincorporated in the apparatus 1, or installed external to the apparatus1. Alternatively, as illustrated in FIG. 1B, the processing statemonitoring unit 2 may be installed at a remote location through anetwork or the like. Further alternatively, as illustrated in FIG. 1B, aportion of its functions may be separated through a network or the like.Specifically, the processing state monitoring unit 2 has the followingconfiguration. First, the processing state monitoring unit 2 has asensor unit 3 for monitoring a processing state in the processingapparatus I involved in the processing of a wafer. The sensor unit 3 isgenerally comprised of several types of sensors. For example, for plasmaprocessing in a plasma etching apparatus, a plasma CVD apparatus and soon, the sensor unit 3 acquires the intensity of emitted light at eachwavelength in a plasma during processing, spectrally resolved using aspectrometer. For example, when using a spectrometer having 1,000channels of CCD array, 1,000 sets of sensed data can be acquired in eachsampling. In addition, the pressure, temperature, gas flow rate, and soon of the apparatus may also be used as sensed data. Also, results ofelectrical measurements made on electric current, voltage, impedance,and harmonic components thereof may be used as sensed data. During theprocessing of a wafer, these sensed data are acquired at proper timeintervals. The acquired sensed data are preserved in a sensed datastorage unit 4. A processed wafer undergoes measurements of processingresults using a processing result measuring device external to orincorporated in the apparatus 1. The measurements of processing resultsmay involve a measurement of CMOS gate width using CDSEM; a measurementof a processed shape such as a cross-sectional shape using across-section SEM; or a measurement of electrical characteristics of aprocessed device. Generally, however, such processing need not beperformed on all wafers, but generally, some of wafers may only beextracted for the measurements of the processing results. The processingstate monitoring unit 2 has a processing result measured value inputunit 5 for receiving measured values of the processing results. Theinput unit 5 may be a reader for reading information recorded on aportable medium such as a floppy disk, CD-ROM and so on, or a wired or awireless network connection device. The measured values of processingresults received from the input device 5 are preserved in a processingresult measured value storage unit 6. The storage unit 6 preservesmeasured values of processing results for each of a variety of devices.A model equation generation unit 7 fetches, from the sensed data storageunit 4 and the processing result measured value storage unit 6, thosesamples of the same types of devices, for which both sensed data andmeasured values of processing results have been preserved therein. Whenthe number of samples is, for example, three or more, the model equationgeneration unit 7 generates a model equation for predicting measuredvalues of processing results using the sensed data as explanatoryvariables. In this event, generally, it is difficult to automaticallyselect sensed data for use in the prediction due to a large number and avariety of types of sensed data acquired in the measurements.Particularly, when a variety of devices are processed, data effectivefor the prediction differs from one device to another, causing itdifficult to previously determine sensors for use in the prediction.

FIG. 5A is a diagram for explaining model equation generation processingbased on a PLS (Partial Least Square) method. As illustrated in FIG. 5A,the PLS method automatically generates from a large number of senseddata an explanatory variable which has the highest correlation tofluctuations in data to be predicted. Simultaneously, a function forcalculating the explanatory variable can be derived from the senseddata. Assume for example that measured values for processing results ofn wafers are intended for the prediction, and Yi represents a measuredvalue for the processing result of an i-th wafer. When m sensed data areacquired from one wafer, Sij indicates a j-th sensed data on the i-thwafer. The m sensed data may be data taken at different times from thesame sensor or taken at the same time from the different sensors. FIG.5B is a diagram for explaining the sensed data Sij. As shown in FIG. 5B,when the processing performed by the apparatus 1 on a single wafer isdivided into three steps with different processing conditions, and threesensors A, B, C are used, Sij may be taken in a range of S11 to Sn9 asshown. Sij may be an average value of sensed data in each step of theprocessing, or in some cases, may be rather converted values from senseddata such as a squared or an inverted version of the sensed data. Withthe use of the PLS method, the sensed data Sij can be converted into mexplanatory variables Xik which is arranged in the order of themagnitude of correlation to fluctuations in the processing resultmeasured value Yi. A function Fk for converting the sensed data Sij intothe explanatory variables Xk is expressed by the following Equation (1):Xik=Fk(Si1, Si2, . . . , Sim)   (1)

Some of the explanatory variable Xik are relied on to predict processingresult measured values. Generally, since an explanatory variable Xil hasthe highest correlation to a processing result measured value Yi, Xi1,Xi2, Xi3 and so on are selected as explanatory variables. In the PLSmethod, a prediction equation such as the following Equation (2) isgenerated simultaneously. However, it may be better case by case tocreate the prediction Equation (2) using explanatory variables such asXil mentioned above.Yi=p(Xi1, Xi2, Xi3)   (2)

On the other hand, the processing result measured values may includedata of wafers which indicate bad wafer processing states and thereforeabnormal processing result measured values. A prediction performed usingnormal multiple regression for such data would result in generation of amodel equation which has a low prediction accuracy due to the influenceof abnormal data, as shown in FIG. 6. To avoid such low predictionaccuracy, robust regression may be used for the prediction. With the useof the robust regression, a correct prediction model equation can begenerated because abnormal data as shown in FIG. 6 are removed from dataintended for prediction as outlier.

FIG. 7 illustrates a flow chart for explaining the model equationcreation processing performed by the model equation generation unit 7.

When there are a large number of types of sensed data, the modelequation generation unit 7 analyzes principal components of the senseddata (steps 701, 702), and performs the robust regression using theresultant principal components to predict processing results (steps705-706). In this event, since explanatory variables include principalcomponents which are not required for the prediction of processingresults, so that principal components with a smallest regressioncoefficient is removed from the explanatory variable (step 707), onemore principal component is added to the explanatory variables (step704), and the multiple regression is again performed (step 706), asillustrated in the flow chart. This loop of processing is repeatedlyexecuted until a prediction error is reduced below a predetermined value(step 708). These regression analyses may be linear, or non-linearregression analysis may be used as derived from physical characteristicsand experimental values of the processing.

The model equation generated by the method as described above ispreserved in a model equation storage unit 8 in FIG. 1. Since the modelequation is generated for each of various types of devices, a number ofmodel equations equal to the number of types of devices processed by theprocessing apparatus 1 are preserved in the model equation storage 8.When a wafer of a certain device is loaded into the processing apparatus1 for processing, a model equation corresponding to this device isloaded into a prediction unit 9. Signals generated from the sensor unit3 during the processing of the device are converted to explanatoryvariables using the equation (1) derived from the PLS method, ifrequired, or converted to principal components through the principalcomponent analysis, and predicted values for processing results arecalculated by the model equation expressed by the equation (2). Thecalculated predicted values are passed to a process recipe control unit10 which changes processing conditions to correct a deviation of thepredicted values from set values for the processing results.

Next, a specific method will be described for correcting processingconditions or input parameters in the process recipe control unit 10.Here, the PLS method is used again. In normal processing ofsemiconductor devices, several conflicting processing performances mayoften be required as requirements for the processing. For example,etching of a gate electrode or the like requires the verticality of sidewalls of the gate electrode as one processing performance, and theetching selectivity of gate polysilicon to underlying gate oxide film asanother processing performance. Specifically, for achieving theverticality of the side walls, etching conditions with scare adhesiveproducts should be used. For achieving high selectivity to theunderlying oxide film, etching conditions with plentiful adhesiveproducts should be used. When there are two conflicting processingperformances as mentioned, the processing conditions or input parametersare difficult to control. FIGS. 8 to 10 are diagrams for explainingprocessing conditions or input parameters for satisfying suchconflicting requirements. Assuming that an aging change of a processingapparatus 1 results in a degraded verticality of the side walls, even ifa reduction in a processing condition or input parameter 1 (here, a flowrate of a gas A), for example, improves the verticality, this reductionsimultaneously causes lower selectivity to the underlying oxide film, sothat this is not preferable as a processing condition or inputparameter.

It is therefore necessary to find a combination of the processingcondition or input parameter 1 and a processing condition or inputparameter 2 (here, wafer bias power) to improve the verticality of theside walls as one processing performance while preventing theselectivity to the underlying oxide film as another processingperformance from degrading.

To solve this problem, as shown in FIG. 8, experimental conditions withdifferent processing conditions or input parameters are set at severalto several tens of points around a central condition under which theprocessing is normally performed, and the processing is performed underthese experimental conditions to measure processing results indicativeof processing performances. Points 1-4 in FIG. 10 correspond to theexperimental conditions 1-4 in FIG. 8. Assume herein that measuredvalues for the verticality of the side walls are taken as processingresult measured values A indicative of processing performance, and anunderlying oxide film selection ratio is taken as processing resultmeasured values B indicative of processing performance.

As illustrated in FIG. 9, the PLS method is applied to this experiment,using a correlation of two processing conditions or input parameters totwo processing result measured values indicative of two differentprocessing performances, to derive a direction A in which the conditionshave the highest correlation to the verticality of the side walls, asshown in FIG. 10. A direction B orthogonal to the direction A, in whichthe conditions have the highest correlation to the selectivity to theunderlying oxide film indicative of one processing performance iscalculated from the direction in which the conditions have the highestcorrelation to the selectivity to the underlying oxide film and which isderived by the PLS method like the above. The condition direction A andcondition direction B have been set in the process recipe control unit10 in FIG. 1A. With these conditions set beforehand, when deterioratedverticality of the side walls is predicted in the prediction unit 9based on the model equation, the processing condition or input parametermay be modified in the condition direction A to improve the verticalityof the side walls indicative of one processing performance withoutscarifying the underlying oxide film selection ratio indicative ofanother processing performance. The calculated condition controldirections are preserved in a process recipe control direction storageunit 14 for use in modifying associated processing conditions or inputparameters when predicted values for processing results calculated bythe model equation deviate from set values.

While the example described herein modifies two processing conditions orinput parameters, the PLS method can modify a larger number ofprocessing conditions or input parameters. As more processing conditionsor input parameters are modified, more preferable results can beprovided. Also, conflicting processing result measured values are notlimited to two, but a larger number of processing results indicative ofa number of processing performances can be taken into account. Forexample, in addition to the verticality of the side walls and theselection ratio to the underlying oxide film indicative of differentprocessing performances, a mask selection ratio or the like indicativeof a further processing performance may be employed.

FIG. 2 illustrates another embodiment of the present invention. Whilethe system of FIG. 2 is substantially identical in configuration to thesystem of FIG. 2, the former differs from the latter in that a predictedvalue display unit 11 is provided instead of the process recipe controlunit 10 to display a message for notifying a fault on a display of theapparatus or the like. The display unit 11 may be implemented by abuzzer for generating an alarm, posting an email, and so on.

FIG. 3 illustrates a further embodiment of the present invention. Thesystems so far described above rely on the prediction of processingresults using a model equation, so that they cannot monitor theprocessing of a device for which no model equation has been generated.Measurements of processing results often take a very long time, so thatmeasurements may be hardly made on all of the processing results. Thus,the model equation cannot be generated for such a device. To addressthis problem, a principal component extraction unit 12 extractsprincipal components from a variety of types of sensed data, and a faultmonitoring unit 13 monitors variations in the principal components todetect a fault in the processing, as illustrated in FIG. 3. If a faultis detected, the apparatus should stop the processing of the next wafer.The fault may be detected using, for example, a variation managementmethod called “SPC” (Statistical Process Control). To implement thismethod, averages and variances of principal components during theprocessing of an intended device are stored to determine that theprocessing is faulty if a measured principal component deviates from itsaverage by several times as much as its variance.

FIG. 4 is a flow chart for explaining a processing flow suitable for aprocessing apparatus which comprises the processing state monitoringunit 2 and a secondary processing state monitoring unit 2′. It is firstdetermined whether or not a model equation has been generated andpreserved for a device intended for processing (step 501). When themodel equation has been preserved, the processing state monitoring unit2 executes monitoring control (step 502). When no model equation hasbeen preserved, the secondary processing state monitoring unit 2′executes the monitoring control (step 503).

The provision of the secondary processing state monitoring unit 2′ usingonly principal components as in FIG. 3 as well as the processing statemonitoring unit 2 using the model equation as in FIGS. 1A and 2 willpermit the monitoring of processing states of either of a device forwhich a model equation has been generated and preserved as illustratedin FIG. 4 and a device for which no model equation has been generated.Such a monitoring system having flexibility is preferred for monitoringa semiconductor processing apparatus.

1. A semiconductor processing apparatus for processing a semiconductorwafer, comprising: a plurality of sensors each for monitoring aprocessing state of said semiconductor processing apparatus; aprocessing result input unit which inputs measured values for processingresults of a semiconductor wafer processed by said semiconductorprocessing apparatus; a model equation generation unit relying on senseddata acquired by said sensors and said measured values to generate amodel equation for predicting a processing result using said sensed dataas an explanatory variable; a processing result prediction unit whichpredicts a processing result based on said model equation and saidsensed data; and a process recipe control unit which compares saidpredicted processing result with a previously set value to control aprocessing condition of said semiconductor processing apparatus suchthat a deviation between said predicted processing result and saidpreviously set value is corrected; wherein a system, which comprisessaid model equation generation unit to thereby generate the predictionmodel equation, is provided at a remote location, and said systemtransmits said generated prediction model equation to said semiconductorprocessing apparatus through a network to control the processingcondition of said semiconductor processing apparatus by said processrecipe control unit.
 2. A semiconductor processing apparatus forprocessing a semiconductor wafer according to claim 1, wherein saidmodel equation unit generates the model equation using a partial leastsquare (PLS) method.
 3. A semiconductor processing apparatus forprocessing a semiconductor wafer according to claim 1, wherein saidmodel equation unit generates the model equation using robust regressionfor measured data in which any abnormal measured data is removed.
 4. Asemiconductor processing apparatus for processing a semiconductor waferaccording to claim 1, wherein a plurality of different performances arecontrolled, and said plurality of different performances includedifferent etching performances.
 5. A semiconductor processing apparatusfor processing a semiconductor wafer according to claim 4, wherein theetching performance includes vertical etching or selectivity.